Automated systems may use video analytics to process video data to determine object movements and other events of concern. Such systems may use background subtraction (BGS) mask processes to distinguish object items as foreground features in the video data, objects that are distinct from a background model of the image data and discernible within video imagery. Information processing systems may receive images or image frame data captured by video cameras or other image capturing devices, wherein individual frames of video data are processed or analyzed by an object detection system in the information processing system to identify and track the presence and movement of objects detected within the video data over time.
Tracking an object as it progress from one camera view to another presents a number of challenges. For example, the same object found within the images of one camera may subsequently possibly appear in the images of any of a group of different cameras that are each located in different locations. Tracking an object requires recognizing which of subsequent possible camera views the object is in, as distinguished from other objects in each of the other camera views. Also, a moving object can transition from one camera into any of many other cameras or remain within any one camera over a variety of different time periods that may each be unpredictable or undeterminable. Further complexities may be introduced if cameras are not placed uniformly, or if objects do not travel at the same speed. Different moving objects may also present similar appearances that may be difficult to distinguish through analyzing visual attributes of the image data, for example one silver sedan may not be distinguishable from another silver sedan based on visual attributes alone, particularly at certain image resolutions or lighting levels.